Computer Aided Classification of Lung Cancer, Ground Glass Lung and Pulmonary Fibrosis Using Machine Learning and KNN Classifier

Respiratory diseases are one of the most prevalent acute and chronic ailments worldwide. According to a recent survey, there were around 545 million cases of chronic respiratory diseases worldwide. Chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD), pneumoconioses, asthma, interstitial lung disease and pulmonary sarcoidosis are significant public health problems across the world. The most significant CRD (Chronic Respiratory Disease) risks have been identified including smoking, contact with indoor and outdoor pollutants, allergies, occupational exposure, poor nutrition, obesity, inactivity and other factors. Interstitial lung diseases are diagnosed on high-resolution computed tomography (HRCT) using a variety of different interstitial pattern namely such as reticular, nodular, reticulonodular, ground-glass lung, cystic, ground-glass with reticular, cystic with ground-glass. If the lung diseases are identified at an early stage life span could be increased. Computer aided diagnosis could play a crucial role in identifying lung diseases at an early stage, disease management and treatment planning. In this paper a novel method is proposed to identify and classify HRCT images of cancerous lung using ML (Machine Learning) and to identify and classify ground glass lung, pulmonary fibrosis lung and healthy lung HRCT images using LBP (Local Binary Pattern) and KNN (K-Nearest Neighbor) classifier. Experimenting the proposed method on 996 images yielded 94% accuracy.

@article{P2024,
  title     = {Computer Aided Classification of Lung Cancer, Ground Glass Lung and Pulmonary Fibrosis Using Machine Learning and KNN Classifier},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Prathibha T P and Punal M Arabi},
  doi       = {10.14569/IJACSA.2024.01507111},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507111}
}